LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna
Unverified ML strategy on Indices by AaravMehta-07. BotFinder score 18 out of 100.
A hybrid AI-based stock market prediction system using LSTM, Random Forest, and XGBoost, built for real-world deployment with Optuna-powered tuning, feature-rich engineering, and e
Source: github
BotFinder analysis pending.
LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna
LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna A hybrid AI-based stock market prediction system using LSTM, Random Forest, and XGBoost, built for real-world deployment with Optuna-powered tuning, feature-rich engineering, and ensemble prediction logic. Designed to optimize F1 score and accuracy, this system aims to generate reliable buy/sell signals on stocks. still work under progress 📈 LSTM + Random Forest + XGBoost Stock Predictor --- 🚀 About the Project This project integrates: - 🔁 Recurrent Neural Networks (LSTM) for sequential financial patterns - 🌲 Random Forest for ensemble-based classification - ⚡ XGBoost for gradient boosting decision trees - 🎯 Optuna for automatic hyperparameter tuning (optional mode) - 📊 Backtesting Module to simulate trading performance ⚙️ Built by a Computer Engineering student to demonstrate real-world ML/AI skills in finance and time series prediction. --- 📌 Features - ✔️ Ensemble of 3 models: LSTM + RF + XGBoost - ✔️ Flag-based retraining (no need to retrain every time) - ✔️ Real stock data from Yahoo Finance - ✔️ Feature-rich engineering: RS
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